Google Cloud Stackdriver and GroundWork Integration

Input and output integration overview

This plugin enables the collection of monitoring data from Google Cloud services through the Stackdriver Monitoring API. It is designed to help users monitor their cloud infrastructure’s performance and health by gathering relevant metrics.

This plugin writes to a GroundWork Monitor instance, allowing for effective metrics management and monitoring in a centralized manner.

Integration details

Google Cloud Stackdriver

The Stackdriver Telegraf plugin allows users to query timeseries data from Google Cloud Monitoring using the Cloud Monitoring API v3. With this plugin, users can easily integrate Google Cloud monitoring metrics into their monitoring stacks. This API provides a wealth of insights about resources and applications running in Google Cloud, including performance, uptime, and operational metrics. The plugin supports various configuration options to filter and refine the data retrieved, enabling users to customize their monitoring setup according to their specific needs. This integration facilitates a smoother experience in maintaining the health and performance of cloud resources and assists teams in making data-driven decisions based on historical and current performance statistics.

GroundWork

The GroundWork plugin enables Telegraf to send metrics to a GroundWork Monitor instance, specifically supporting GW8 and newer versions. This integration allows users to leverage the robust monitoring capabilities of GroundWork, enabling comprehensive oversight of metrics collected from diverse sources. Users can specify various parameters such as the GroundWork instance URL, agent IDs, and authentication credentials, allowing for a tailored fit within their existing monitoring setups. It also supports secret-store secrets to enhance security for sensitive fields like username and password. Tags used within the plugin provide fine-grained control over how metrics are categorized and displayed within the GroundWork interface, allowing for custom configurations that adapt to different monitoring needs. However, users should be aware that string metrics are currently not supported by GroundWork, impacting how they manage their data.

Configuration

Google Cloud Stackdriver

[[inputs.stackdriver]]
  ## GCP Project
  project = "erudite-bloom-151019"

  ## Include timeseries that start with the given metric type.
  metric_type_prefix_include = [
    "compute.googleapis.com/",
  ]

  ## Exclude timeseries that start with the given metric type.
  # metric_type_prefix_exclude = []

  ## Most metrics are updated no more than once per minute; it is recommended
  ## to override the agent level interval with a value of 1m or greater.
  interval = "1m"

  ## Maximum number of API calls to make per second.  The quota for accounts
  ## varies, it can be viewed on the API dashboard:
  ##   https://cloud.google.com/monitoring/quotas#quotas_and_limits
  # rate_limit = 14

  ## The delay and window options control the number of points selected on
  ## each gather.  When set, metrics are gathered between:
  ##   start: now() - delay - window
  ##   end:   now() - delay
  #
  ## Collection delay; if set too low metrics may not yet be available.
  # delay = "5m"
  #
  ## If unset, the window will start at 1m and be updated dynamically to span
  ## the time between calls (approximately the length of the plugin interval).
  # window = "1m"

  ## TTL for cached list of metric types.  This is the maximum amount of time
  ## it may take to discover new metrics.
  # cache_ttl = "1h"

  ## If true, raw bucket counts are collected for distribution value types.
  ## For a more lightweight collection, you may wish to disable and use
  ## distribution_aggregation_aligners instead.
  # gather_raw_distribution_buckets = true

  ## Aggregate functions to be used for metrics whose value type is
  ## distribution.  These aggregate values are recorded in in addition to raw
  ## bucket counts; if they are enabled.
  ##
  ## For a list of aligner strings see:
  ##   https://cloud.google.com/monitoring/api/ref_v3/rpc/google.monitoring.v3#aligner
  # distribution_aggregation_aligners = [
  #  "ALIGN_PERCENTILE_99",
  #  "ALIGN_PERCENTILE_95",
  #  "ALIGN_PERCENTILE_50",
  # ]

  ## Filters can be added to reduce the number of time series matched.  All
  ## functions are supported: starts_with, ends_with, has_substring, and
  ## one_of.  Only the '=' operator is supported.
  ##
  ## The logical operators when combining filters are defined statically using
  ## the following values:
  ##   filter ::=  {AND  AND  AND }
  ##   resource_labels ::=  {OR }
  ##   metric_labels ::=  {OR }
  ##   user_labels ::=  {OR }
  ##   system_labels ::=  {OR }
  ##
  ## For more details, see https://cloud.google.com/monitoring/api/v3/filters
  #
  ## Resource labels refine the time series selection with the following expression:
  ##   resource.labels. = 
  # [[inputs.stackdriver.filter.resource_labels]]
  #   key = "instance_name"
  #   value = 'starts_with("localhost")'
  #
  ## Metric labels refine the time series selection with the following expression:
  ##   metric.labels. = 
  #  [[inputs.stackdriver.filter.metric_labels]]
  #    key = "device_name"
  #    value = 'one_of("sda", "sdb")'
  #
  ## User labels refine the time series selection with the following expression:
  ##   metadata.user_labels."" = 
  #  [[inputs.stackdriver.filter.user_labels]]
  #    key = "environment"
  #    value = 'one_of("prod", "staging")'
  #
  ## System labels refine the time series selection with the following expression:
  ##   metadata.system_labels."" = 
  #  [[inputs.stackdriver.filter.system_labels]]
  #    key = "machine_type"
  #    value = 'starts_with("e2-")'
</code></pre>

GroundWork

[[outputs.groundwork]]
  ## URL of your groundwork instance.
  url = "https://groundwork.example.com"

  ## Agent uuid for GroundWork API Server.
  agent_id = ""

  ## Username and password to access GroundWork API.
  username = ""
  password = ""

  ## Default application type to use in GroundWork client
  # default_app_type = "TELEGRAF"

  ## Default display name for the host with services(metrics).
  # default_host = "telegraf"

  ## Default service state.
  # default_service_state = "SERVICE_OK"

  ## The name of the tag that contains the hostname.
  # resource_tag = "host"

  ## The name of the tag that contains the host group name.
  # group_tag = "group"

Input and output integration examples

Google Cloud Stackdriver

1. Integrating Cloud Metrics into Custom Dashboards: With this plugin, teams can funnel metrics from Google Cloud into personalized dashboards, allowing for real-time monitoring of application performance and resource utilization. By customizing the visual representation of cloud metrics, operations teams can easily identify trends and anomalies, enabling proactive management before issues escalate.

2. Automated Alerts and Analysis: Users can set up automated alerting mechanisms leveraging the plugin’s metrics to track resource thresholds. This capability allows teams to act swiftly in response to performance degradation or outages by providing immediate notifications, thus reducing the mean time to recovery and ensuring continued operational efficiency.

3. Cross-Platform Resource Comparison: The plugin can be used to draw metrics from various Google Cloud services and compare them with on-premise resources. This cross-platform visibility helps organizations make informed decisions about resource allocation and scaling strategies, as well as optimize cloud spending versus on-premise infrastructure.

4. Historical Data Analysis for Capacity Planning: By collecting historical metrics over time, the plugin empowers teams to conduct thorough capacity planning. Understanding past performance trends facilitates accurate forecasting for resource needs, leading to better budgeting and investment strategies.

GroundWork

1. Centralized Monitoring Dashboard: Use the GroundWork plugin to aggregate metrics from multiple Telegraf instances into a single GroundWork Monitor dashboard. This configuration offers complete visibility into system health across various components, enabling swift identification of performance bottlenecks and improved incident response times.

2. Service Health Monitoring with Alerts: Configure this plugin to send critical service metrics to GroundWork, establishing a robust alerting system. Metrics such as CPU usage and service statuses can trigger alerts based on threshold values, informing administrators of potential issues before they escalate, thereby enhancing system reliability.

3. Historical Data Analysis: Leverage the historical metric capabilities of GroundWork using this plugin to conduct trend analysis over time. This application allows organizations to make data-driven decisions based on comprehensive historical performance metrics, which can assist in capacity planning and optimize resource allocation.

4. Custom Service Tags for Enhanced Monitoring: Extend the functionality of this plugin by utilizing custom tags for different services and hosts. By customizing these tags, users can filter and categorize metrics more effectively within their monitoring framework, leading to tailored monitoring experiences that align specifically with business objectives.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.